| 1. |
- al-Dbissi, Moad, 1994, et al.
(författare)
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Conceptual design and initial evaluation of a neutron flux gradient detector
- 2022
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Ingår i: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. - : Elsevier BV. - 0168-9002. ; 1026
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Tidskriftsartikel (refereegranskat)abstract
- Identification of the position of a localized neutron source, or that of local inhomogeneities in a multiplying or scattering medium (such as the presence of small, strong absorbers) is possible by measurement of the neutron flux in several spatial points, and applying an unfolding procedure. It was suggested earlier, and it was confirmed by both simulations and pilot measurements, that if, in addition to the usually measured scalar (angularly integrated) flux, the neutron current vector or its diffusion approximation (the flux gradient vector) is also considered, the efficiency and accuracy of the unfolding procedure is significantly enhanced. Therefore, in support of a recently started project, whose goal is to detect missing (replaced) fuel pins in a spent fuel assembly by non-intrusive methods, this idea is followed up. The development and use of a dedicated neutron detector for within-assembly measurements of the neutron scalar flux and its gradient are planned. The detector design is based on four small, fiber-mounted scintillation detector tips, arranged in a rectangular pattern. Such a detector is capable of measuring the two Cartesian components of the flux gradient vector in the horizontal plane. This paper presents an initial evaluation of the detector design, through Monte Carlo simulations in a hypothetical scenario.
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| 2. |
- al-Dbissi, Moad, 1994, et al.
(författare)
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Identification of diversions in spent PWR fuel assemblies by PDET signatures using Artificial Neural Networks (ANNs)
- 2023
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Ingår i: Annals of Nuclear Energy. - 0306-4549 .- 1873-2100. ; 193
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Tidskriftsartikel (refereegranskat)abstract
- Spent nuclear fuel represents the majority of materials placed under nuclear safeguards today and it requires to be inspected and verified regularly to promptly detect any illegal diversion. Research is ongoing both on the development of non-destructive assay instruments and methods for data analysis in order to enhance the verification accuracy and reduce the inspection time. In this paper, two models based on Artificial Neural Networks (ANNs) are studied to process measurements from the Partial Defect Tester (PDET) in spent fuel assemblies of Pressurized Water Reactors (PWRs), and thus to identify at different levels of detail whether nuclear fuel has been replaced with dummy pins or not. The first model provides an estimation of the percentage of replaced fuel pins within the inspected fuel assembly, while the second model determines the exact configuration of the replaced fuel pins. The two models are trained and tested using a dataset of Monte-Carlo simulated PDET responses for intact spent PWR fuel assemblies and a variety of hypothetical diversion scenarios. The first model classifies fuel assemblies according to the percentage of diverted fuel with a high accuracy (96.5%). The second model reconstructs the correct configuration for 57.5% of the fuel assemblies available in the dataset and still retrieves meaningful information of the diversion pattern in many of the misclassified cases.
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| 3. |
- al-Dbissi, Moad, 1994, et al.
(författare)
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On the use of neutron flux gradient with ANNs for the detection of diverted spent nuclear fuel
- 2024
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Ingår i: Annals of Nuclear Energy. - 0306-4549 .- 1873-2100. ; 204
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Tidskriftsartikel (refereegranskat)abstract
- One of the main tasks in nuclear safeguards is regular inspections of Spent Nuclear Fuel (SNF) assemblies to detect possible diversions of special nuclear material such as 235U and 239Pu. In these inspections, characteristic signatures of SNF such as emissions of neutrons and gamma rays from the radioactive decay, are measured and their consistency with the declared assemblies is verified to ensure that no fuel pins have been removed. Research in this field is focused on both the development of detection equipment and methods for the analysis of the acquired measurement data. In this paper, the use of the neutron flux gradient, which is not considered in regular SNF verification, is investigated in combination with the scalar neutron flux as input to artificial neural network models for the quantification of fuel pins in SNF assemblies. The training and testing of these ANN models rely on a synthetic dataset that is generated from Monte Carlo simulations of a typical intact pressurized water reactor assembly and with different patterns of fuel pins replaced by dummy pins. The dataset consists of unique scenarios so that the ANN can be assessed over “unknown” cases that are not part of the learning phase. Results show that the neutron flux gradient is advantageous for a more accurate reconstruction of diversions within SNF assemblies.
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| 4. |
- Anderson, Johan, 1973, et al.
(författare)
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Derivation and quantitative analysis of the differential self-interrogation Feynman-alpha method
- 2012
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Ingår i: European Physical Journal Plus. - : Springer Science and Business Media LLC. - 2190-5444. ; 127:2, s. 1-6
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Tidskriftsartikel (refereegranskat)abstract
- A stochastic theory for a branching process in a neutron population with two energy levels is used to assess the applicability of the differential self-interrogation Feynman-alpha method by numerically estimated reaction intensities from Monte Carlo simulations. More specifically, the variance to mean or Feynman-alpha formula is applied to investigate the appearing exponentials using the numerically obtained reaction intensities.
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| 5. |
- Anderson, Johan, 1973, et al.
(författare)
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Derivation and quantitative analysis of the differential self-interrogation Feynman-alpha method
- 2011
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Ingår i: Proceedings 52nd INMM Conference 17-21 July, Palm Desert, CA, USA (2011).
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Konferensbidrag (refereegranskat)abstract
- A stochastic theory for a branching process in a neutronpopulation with two energy levels is used to assess theapplicability of the differential self-interrogation Feynman-alpha method by numerically estimated reaction intensities from Monte Carlo simulations. More specifically, the variance to mean or Feynman-alpha formula is applied to investigate the appearing exponentials using the numerically obtained reaction intensities.
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| 6. |
- Anderson, Johan, 1973, et al.
(författare)
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On the Feynman-alpha formula for fast neutrons
- 2011
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Ingår i: Proceedings 33rd ESARDA 16-20 May, Budapest, Hungary (2011).
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Konferensbidrag (refereegranskat)abstract
- In this contribution, a stochastic theory for a branching process in a neutron population with two energy levels is investigated. In particular, a variance to mean or Feynman-alpha formula is derived in this generalized scenario using the Kolmogorov forward or master equation theory for the probabilities in a system with a compound Poisson source.
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| 7. |
- Anderson, Johan, 1973, et al.
(författare)
-
Two-point theory for the differential self-interrogation Feynman-alpha method
- 2012
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Ingår i: European Physical Journal Plus. - : Springer Science and Business Media LLC. - 2190-5444. ; 127:8, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- A Feynman-alpha formula has been derived in a two region domain pertaining the stochastic differential self-interrogation (DDSI) method and the differential die-away method (DDAA). Monte Carlo simulations have been used to assess the applicability of the variance to mean through determination of the physical reaction intensities of the physical processes in the two domains. More specifically, the branching processes of the neutrons in the two regions are described by the Chapman-Kolmogorov equation, including all reaction intensities for the various processes, that is used to derive a variance to mean relation for the process. The applicability of the Feynman-alpha or variance to mean formulae are assessed in DDSI and DDAA of spent fuel configurations.
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| 8. |
- Andersson, Tell, et al.
(författare)
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Development and application of core diagnostics and monitoring for the Ringhals PWRs
- 2003
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Ingår i: Progress in Nuclear Energy. - 0149-1970. ; 43:1-4, s. 35-41
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Tidskriftsartikel (refereegranskat)abstract
- Noise analysis and reactor diagnostics have been applied at the Ringhals PWRs for a long time. Through a collaboration with the Department of Reactor Physics, Chalmers University of Technology, methods for treating new problems were elaborated, and known methods were developed further to make them more effective or to suit specific applications. All these methods were tested in real measurements, and many of them have been used routinely afterwards. In this paper two particular new methods are described in detail: 1) the determination of the axial position of control rods from the axial shape of the neutron flux with neural network methods, and 2) the use of gamma thermometers for the determination of the MTC and for core flow estimation.
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| 9. |
- Avdic, Senada, et al.
(författare)
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Item identification with a space-dependent model of neutron multiplicities and artificial neural networks
- 2023
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Ingår i: Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. - 0168-9002. ; 1057
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Tidskriftsartikel (refereegranskat)abstract
- A method of calculating the neutron multiplicity rates (singles, doubles and triples rates), based on transport theory, was developed by us recently. The model treats the full 3-D spatial transport and multiplication of neutrons, accounting also for the shape of the item and the spatial distribution of the source, in one-speed theory. For a given item and its source distribution, the model can predict the multiplicity rates more precisely than the point model, on which traditional neutron multiplicity counting is based. However, so far it has not been investigated how the enhanced accuracy of the calculated multiplicity rates (i.e. the solution of the direct task) can be used to estimate the parameters of interest of the measurement item, primarily the fission rate (the solution of the inverse task). Unlike for the point model, the multiplicity rates under the extended scheme can only be given numerically, as solutions of integral transport equations, and hence an analytical inversion of the formulae is not possible. In this work it is investigated how machine learning methods, primarily the use of artificial neural networks, which only need numerical values of the solution of the direct task (the multiplicity rates), can be used for this purpose. It is shown that for numerical test items containing a mixture of 239Pu and 240Pu, the fraction of the latter varying between 4% and 25%, one can extract the masses of both isotopes from a properly trained network.
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| 10. |
- Avdic, Senada, et al.
(författare)
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Unfolding sample parameters from neutron and gamma multiplicities using artificial neural networks.
- 2009
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Ingår i: ESARDA Bulletin. - 0392-3029. ; :43, s. 21-29
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Tidskriftsartikel (refereegranskat)abstract
- Expressions for neutron and gamma factorial momentshave been known in the literature. The neutronfactorial moments have served as the basis of constructinganalytic expressions for the detection ratesof singles, doubles and triples, which can be used tounfold sample parameters from the measured neutronmultiplicity rates. The gamma factorial momentscan also be extended into detection rates of multiplets,as well as the combined use of joint neutronand gamma multiplicities and the corresponding detectionrates. Counting up to third order, there arenine auto- and cross factorial moments.Adding the gamma counting to the neutrons introducesnew unknowns, related to gamma generation,leakage, and detection. Despite of having more unknowns,the total number of independent measurablemoments exceeds the number of unknowns. On theother hand, the structure of the additional equationsis substantially more complicated than that of theneutron moments, hence the analytical inversion ofthe gamma moments alone is not possible.We suggest therefore to invert the non-linear systemof over-determined equations by using artificialneural networks (ANN), which can handle both thenon-linearity and the redundancies in the measuredquantities in an effective and accurate way. The useof ANN is successfully demonstrated on the unfoldingof neutron multiplicity rates for the sample fissionrate, the leakage multiplication and the ratio.The analysis is further extended to unfold also thegamma related parameters. The stability and robustnessof the ANNs is further investigated to verify theapplicability of the method. The ANN approach enablesextraction of additional important informationon the fissile sample compared to the application ofthe analytical method.
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